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% Encoding: MacRoman

@book{agent2005,
   author = {Luck, McBurney and Shehory, Willmott},
   title = {Agent Technology. Computing as Interaction. A Roadmap for Agent Based Computing},
   year = 2005,
   url = {http://www.agentlink.org/roadmap/},
}

@book{nash94,
   author = {M. Rubenstein and M. Osborne},
   title = { A Course in Game Theory},
   publisher={MIT Press},
   year = 1994,
}

@book{Osborne1994,
   author = {Osborne, Martin J. and Ariel Rubinstein},
   title = {A Course in Game Theory},
   year = 1994,
   address = {Cambridge, MA},
   publisher = {MIT Press},
}

@book{fudenberg1993game,
  title={Game theory},
  author={Fudenberg, D. and Tirole, J.},
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  year=1993,
  publisher={MIT Press},
  chapter ={1},
}

@Book{sullivan2003economics,
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 title = {Economics : principles in action},
 publisher = {Prentice Hall},
 year = {2003},
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 @book{dawkins1976,
	Address = {New York},
	Author = {Dawkins, Richard},
	Booktitle = {The Selfish Gene},
	Date-Added = {2006-10-08 19:02:30 -0400},
	Date-Modified = {2011-07-14 19:48:18 -0400},
	Publisher = {Oxford University Press},
	Title = {The Selfish Gene},
	Year = {1976}
	}
 
 @book{begg2006economics,
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  year={2006},
  publisher={McGraw-Hill Education}
}

 @book{axelrod1987,
  title={The evolution of strategies in the iterated prisoner's dilemma},
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  year={1987},
  publisher={CA-Morgan Kaufmann},
}
 @article{experts,
    author    = {Daniela Pucci De Farias (MIT) and Nimrod Megiddo (IBM Almaden Research Center)},
    title     = {How to Combine Expert (or Novice) Advice when Actions Impact the Environment }, 
    journal   = {ACM},
    year      = 2005,
}
 @article{experts06,
    author    = {Daniela Pucci De Farias (MIT) and Nimrod Megiddo (IBM Almaden Research Center)},
    title     = {Combining Expert Advice in Reactive Environments}, 
    journal   = {ACM},
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    year      = 2006,
}

 @article{mr1,
    author    = {Auer P. Cesa-Bianchi N, Freund Y. and Schapire},
    title     = {Gambling in a rigged casino. The adversarial multi-armed bandit problem}, 
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}
 @book{mr2,
    author    = {Freund,Y. and Schapire},
    title     = {Adaptive game playing using multiplicative weights. Games and Economic Behavior }, 
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    year      = 1999,
}

@article{shashi,
    author    = {Shashi Mittal and Kalyanmoy Deb},
    title     = {Optimal Strategies of the Iterated Prisoner's Dilemma Problem for Multiple Conflicting Objectives}, 
    journal   = {MIT Press},
    year      = 2005,
}

 @misc{niceguys,
	author = {Dawkins, Richard},
	title = {Nice Guys Finish First (BBC Horizon Documentary)},
	publisher = {BBC},
	year = 1986,
	}
	
	@article{nsgaii,
    abstract = {{Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN s) computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing parameter. In this paper, we suggest a non-dominated sorting based multi-objective evolutionary algorithm (we called it the Non-dominated Sorting GA-II or NSGA-II) which alleviates all the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN 2) computational complexity is presented. Second, a selection operator is presented which creates a mating pool by combining the parent and child populations and selecting the best (with respect to fitness and spread) N solutions. Simulation results on a number of difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA two other elitist multi-objective EAs which pay special attention towards creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint non-linear problem, are compared with another constrained multi-objective optimizer and much better performance of NSGA-II is observed. Because of NSGA-II\&\#039;s low computational requirements, elitist approach, parameter-less niching approach, and simple constraint-handling strategy, NSGA-II should find increasing applications in the coming years.}},
    author = {Deb, Kalyanmoy and Pratap, Amrit and Agarwal, Sameer and Meyarivan, T.},
    citeulike-article-id = {3131706},
    citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.7210},
    journal = {IEEE Transactions on Evolutionary Computation},
    keywords = {genetic\_algorithms},
    pages = {182--197},
    posted-at = {2008-08-18 11:59:06},
    priority = {0},
    title = {{A Fast Elitist Multi-Objective Genetic Algorithm: NSGA-II}},
    url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.7210},
    volume = {6},
    year = {2000}
}



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